Weld X-ray Image Defect Recognition Based on Deep Learning
With the increasing requirements for welding quality in industrial manufacturing,accurate detection and identification of weld defects become the important parts to ensure the safety of the structure.Traditional detection methods are inefficient and inaccuracy in processing complex weld images.This paper proposes a method of weld defect identification based on X-ray images,with a focus on the defect feature extraction and the application of random forest model.We use image preprocessing to improve image quality,and accurate extraction of weld bead areas.Subsequently,by using various feature extraction techniques to defect classification and identification,we construct and optimize the random forest model to evaluate its performance.The experiment shows that the model performs excellently in identifying cracks and pores,with accuracy rates of 91.0％and 84.6％,respectively,which is superior to the BP neural network.This study provides a comprehensive and effective solution for the evaluation of weld quality,which is of great significance to welding engineering.
万方数据
维普
